With the great economic costs and traditionally poor outcomes among chronic temporomandibular joint and muscle disorder (TMJMD) patients, it has become important to treat patients in the acute state, in order to prevent these more chronic disability problems. This has been the goal of two past funded grant projects. Results of the initial project isolated risk factors that successfully predicted the development of chronicity, with a 91% accuracy rate. A statistical algorithm was developed which was used in the second project to screen out high-risk patients. These patients were then randomly assigned an early intervention or non-intervention group. One-year follow-up evaluations documented the treatment efficacy and cost effectiveness of early intervention. These results have major implications for effective early intervention and significant health-care cost savings for this prevalent pain and disability problem. For the present proposed project, we plan to implement this treatment program in order to evaluate its effectiveness in more community-based dental practices. This is in response to NIH's request for the implementation of evidence-based treatment approaches, developed in controlled clinical settings, to the real world of diverse practices in the community. Acute TMJMD patients will be recruited from two community clinics. Based upon our risk screening algorithm, high-risk patients will be randomly assigned to one of two groups (n=225/group): an early biobehavioral intervention or an attention-control group. It is hypothesized that the attention control high-risk patients will display more chronic TMJMD problems, relative to the high-risk early intervention patients, at one-year follow-up. Another aim of the project is to evaluate the potential utility of new technologies that will lead to even greater precision in detecting individual differences among TMJMD patients. Measures evaluated include chewing performance, cortisol levels, and other biopsychosocial outcomes. This represents the next step in comprehensively understanding brain-behavior patterns in TMJMD. Also, structural equation modeling methodology will be used. Such a multi-level, multi-systems approach has not been applied to better understand the biopsychosocial underpinnings of TMJMD. Results from this component of the project will greatly aid in stimulating future research leading to the better understanding of TMJMD, as well as better tailoring of prescribed treatment regimens.